In their groundbreaking work, Professor Pados and his students
describe for the first time in the literature ways to define and
calculate optimally L1-norm signal subspaces. In folk language,
L1-norm distance is sometimes referred to as "Manhattan distance"
or "Taxicab." L1-calculated signal subspaces are less sensitive to
outlying (erroneous) data than L2-calculated subspaces. The work
suggests the possibility now of establishing a new practical line
of L1 data Principal-Component-Analysis (PCA) that parallels and
robustifies the familiar L2 PCA approaches. Applications are
projected to be as broad and as diverse as PCA itself, for example
tweet analysis, astronomical data analysis, genomic signal
processing, robust multidimensional statistical characterization
(materials, processes), machine learning, feature extraction, image
processing, spectrum sensing.